Project: NWU_101176
Pipeline Version:
Cellecta, Inc
The DriverMap Adaptive Immune Receptor (AIR) Repertoire Profiling Service from Cellecta provides you with a profile of all TCR and BCR CDR3 or full-length variable regions in blood, cell, or RNA samples. With the DriverMap AIR TCR-BCR Profiling Service, you get a larger complement of clonotypes than other similar assays, reproducible and comprehensive coverage from a range of immune sample inputs, including total RNA from whole blood and Rapid, 1-month turnaround from sample submission to an extensive analysis report
Since T- and B-cells work synergistically in the adaptive immune response, Cellecta has designed an assay that profiles both T-cell receptor (TCR) and B-cell receptor (BCR) repertoires in a single convenient reaction. Separate assays specific for T- or B-cell chains are also available. The DriverMap AIR-RNA assay quantifies T-cell and B-cell receptor transcripts. It is designed to specifically amplify only functional RNA molecules from human or mouse TCR and BCR cells, avoiding non-functional pseudogenes with similar structures or full-length variable regions from human RNA molecules enables highly sensitive detection of low-frequency, rare TCR and BCR clonotypes and more comprehensive profiling when working with small samples and limited numbers of cells. The DriverMap AIR-DNA assay amplifies receptor genes directly from genomic DNA. The AIR-DNA assay provides a more quantitative measurement of the genetic copies for each CDR3-specific clonotype which correlates to the number of cells with that clonotype in that sample. This data enables the measurement of clonal expansion in T and B cells. Combining data obtained from both the AIR-DNA and AIR-RNA assays enables assessment of both the transcriptional activation and number of cells with a particular clonotype. The ability to differentiate these two effects provides a quantitative basis to assess antigen-activated clonotypes
Applications of BCR sequencing: Identify broadly neutralizing
antibodies (BNAbs) and map Ig-seq datasets to known antibody structures
for antibody and vaccine development, Track B-cell migration and
development patterns, Find markers of autoimmune diseases such as
multiple sclerosis, rheumatoid arthritis and cancers (e.g. B-cell
lymphoma), and Contrast na
Applications of TCR sequencing: Track T-cell clonality and diversity for insights into mechanisms of action of immune checkpoint inhibitors for immunotherapies, Assess TCR overlap between repertoires to define spatial and temporal heterogeneity of the anti-tumoral immune response, and Analyze TCR sequence and structure to annotate antigenic specificity for developing personalized cellular immunotherapies
How is the DriverMap AIR Assay Different from other Adaptive Immune Receptor Repertoire (AIRR) Assays?
DriverMap
DriverMap Adaptive Imumune Repertoire (AIR) profiling Assay workflow is as follow:
The protocol in this section describe how to extract T- and B- Cell receptor repertoire from NGS data generated from the Cellecta DriverMap AIR kit. MiXCR is used to analyze NGS data, extract clonotype and obtain various plots and tabular results. More details can be abtained from MiXCR official website (MiXCR Cellecta Preset). immunarch is an R package designed to analyse T-cell receptor (TCR) and B-cell receptor (BCR) repertoires. Post analysis can be done by MiXCR or immunarch. The following bioinformatics workflow is recommended for DriverMap AIR assay:
| sample | pct.dup | pct.gc | tot.seq | seq.length |
|---|---|---|---|---|
| 10_113046_R1 | 97.46 | 53 | 13029992 | 148 |
| 10_113046_R2 | 91.57 | 52 | 13029992 | 148 |
| 11_113047_R1 | 97.58 | 55 | 6216885 | 148 |
| 11_113047_R2 | 94.82 | 54 | 6216885 | 148 |
| 1_113026_R1 | 97.42 | 53 | 9004834 | 148 |
| 1_113026_R2 | 95.94 | 52 | 9004834 | 148 |
| 2_113030_R1 | 95.43 | 56 | 1416088 | 148 |
| 2_113030_R2 | 83.55 | 56 | 1416088 | 148 |
| 3_113032_R1 | 97.41 | 53 | 5922502 | 148 |
| 3_113032_R2 | 93.80 | 52 | 5922502 | 148 |
| 4_113033_R1 | 96.56 | 53 | 1757522 | 148 |
| 4_113033_R2 | 93.68 | 52 | 1757522 | 148 |
| 5_113034_R1 | 96.13 | 55 | 2811555 | 148 |
| 5_113034_R2 | 89.55 | 54 | 2811555 | 148 |
| 6_113037_R1 | 97.77 | 54 | 9302919 | 148 |
| 6_113037_R2 | 94.99 | 53 | 9302919 | 148 |
| 7_113038_R1 | 97.70 | 54 | 11911843 | 148 |
| 7_113038_R2 | 95.98 | 53 | 11911843 | 148 |
| 8_113042_R1 | 97.34 | 53 | 12495147 | 148 |
| 8_113042_R2 | 94.51 | 52 | 12495147 | 148 |
| 9_113044_R1 | 97.47 | 54 | 13514357 | 148 |
| 9_113044_R2 | 92.05 | 53 | 13514357 | 148 |
pct.dup = Sequence Duplication Rate
pct.gc = GC Percentage
tot.seq = Total Number of Reads
seq.length = Sequencing Length
(NT)
| sample | nb_problems | module |
|---|---|---|
| 2_113030_R1 | 5 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences, Adapter Content |
| 2_113030_R2 | 5 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences, Adapter Content |
| 3_113032_R1 | 5 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences, Adapter Content |
| 3_113032_R2 | 5 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences, Adapter Content |
| 5_113034_R1 | 5 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences, Adapter Content |
| 5_113034_R2 | 5 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences, Adapter Content |
| 11_113047_R1 | 4 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences |
| 11_113047_R2 | 4 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences |
| 4_113033_R1 | 4 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences |
| 4_113033_R2 | 4 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences |
| 6_113037_R1 | 4 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences |
| 6_113037_R2 | 4 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels, Overrepresented sequences |
| 10_113046_R1 | 3 | Per base sequence content, Sequence Duplication Levels, Overrepresented sequences |
| 10_113046_R2 | 3 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels |
| 1_113026_R1 | 3 | Per base sequence content, Sequence Duplication Levels, Overrepresented sequences |
| 1_113026_R2 | 3 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels |
| 7_113038_R1 | 3 | Per base sequence content, Sequence Duplication Levels, Overrepresented sequences |
| 7_113038_R2 | 3 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels |
| 8_113042_R1 | 3 | Per base sequence content, Sequence Duplication Levels, Overrepresented sequences |
| 8_113042_R2 | 3 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels |
| 9_113044_R1 | 3 | Per base sequence content, Sequence Duplication Levels, Overrepresented sequences |
| 9_113044_R2 | 3 | Per base sequence content, Per sequence GC content, Sequence Duplication Levels |
nb_problems = Number of criteria that failed
module = List of
criteria that failed
| 10_113046_R1 | 10_113046_R2 | 11_113047_R1 | 11_113047_R2 | 1_113026_R1 | 1_113026_R2 | 2_113030_R1 | 2_113030_R2 | 3_113032_R1 | 3_113032_R2 | 4_113033_R1 | 4_113033_R2 | 5_113034_R1 | 5_113034_R2 | 6_113037_R1 | 6_113037_R2 | 7_113038_R1 | 7_113038_R2 | 8_113042_R1 | 8_113042_R2 | 9_113044_R1 | 9_113044_R2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Basic Statistics | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS |
| Per base sequence quality | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS |
| Per tile sequence quality | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS |
| Per sequence quality scores | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS |
| Per base sequence content | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL |
| Per sequence GC content | WARN | FAIL | FAIL | FAIL | PASS | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | PASS | FAIL | WARN | FAIL | PASS | FAIL |
| Per base N content | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS |
| Sequence Length Distribution | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS | PASS |
| Sequence Duplication Levels | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL |
| Overrepresented sequences | FAIL | WARN | FAIL | FAIL | FAIL | WARN | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | FAIL | WARN | FAIL | WARN | FAIL | WARN |
| Adapter Content | PASS | PASS | WARN | WARN | PASS | PASS | FAIL | FAIL | FAIL | FAIL | WARN | WARN | FAIL | FAIL | WARN | WARN | PASS | PASS | PASS | PASS | PASS | PASS |
| 10_113046 | 11_113047 | 1_113026 | 2_113030 | 3_113032 | 4_113033 | 5_113034 | 6_113037 | 7_113038 | 8_113042 | 9_113044 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Successfully aligned reads: | OK | WARN | OK | ALERT | WARN | WARN | ALERT | OK | OK | OK | OK |
| Off target (non TCR/IG) reads: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| Reads with no V or J hits: | OK | WARN | OK | ALERT | WARN | WARN | ALERT | WARN | OK | OK | OK |
| Reads with no barcode: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| Overlapped paired-end reads: | OK | OK | OK | ALERT | OK | OK | WARN | OK | OK | OK | OK |
| Alignments that do not cover VDJRegion: | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Tag groups that do not cover VDJRegion: | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Barcode collisions in clonotype assembly: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| Unassigned alignments in clonotype assembly: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| Reads used in clonotypes: | OK | WARN | OK | ALERT | WARN | WARN | ALERT | WARN | OK | OK | OK |
| Alignments dropped due to low sequence quality: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| Alignments clustered in PCR error correction: | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Clonotypes clustered in PCR error correction: | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Clones dropped in post-filtering: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| Alignments dropped in clones post-filtering: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| Reads dropped in tags error correction and filtering: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| UMIs artificial diversity eliminated: | ALERT | ALERT | ALERT | ALERT | ALERT | ALERT | ALERT | ALERT | ALERT | ALERT | ALERT |
| Reads dropped in UMI error correction and whitelist: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| Reads dropped in tags filtering: | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| 10_113046 | 11_113047 | 1_113026 | 2_113030 | 3_113032 | 4_113033 | 5_113034 | 6_113037 | 7_113038 | 8_113042 | 9_113044 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Successfully aligned reads: | 98.34% | 82.76% | 92.98% | 16.39% | 80.22% | 84.58% | 53.48% | 87.38% | 95.79% | 96.2% | 98.23% |
| Off target (non TCR/IG) reads: | 0.22% | 1.48% | 0.5% | 5.94% | 1.79% | 1.04% | 4.4% | 0.41% | 0.32% | 0.22% | 0.39% |
| Reads with no V or J hits: | 1.29% | 15.72% | 6.47% | 77.66% | 17.95% | 14.33% | 42.09% | 12.17% | 3.84% | 3.32% | 1.25% |
| Reads with no barcode: | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
| Overlapped paired-end reads: | 99.03% | 95.91% | 98.7% | 79.32% | 94.5% | 97.34% | 88.44% | 96.75% | 98.98% | 98.79% | 99.01% |
| Alignments that do not cover VDJRegion: | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Tag groups that do not cover VDJRegion: | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Barcode collisions in clonotype assembly: | 0.12% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.1% | 0.06% | 0.09% |
| Unassigned alignments in clonotype assembly: | 0.91% | 0.32% | 1.14% | 0.16% | 0.26% | 0.22% | 0.26% | 0.37% | 0.77% | 0.33% | 0.92% |
| Reads used in clonotypes: | 96.16% | 82.21% | 91.49% | 16.32% | 79.69% | 83.99% | 53.16% | 86.58% | 93.72% | 94.84% | 95.99% |
| Alignments dropped due to low sequence quality: | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
| Alignments clustered in PCR error correction: | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Clonotypes clustered in PCR error correction: | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Clones dropped in post-filtering: | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
| Alignments dropped in clones post-filtering: | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
| Reads dropped in tags error correction and filtering: | 0.81% | 0.35% | 0.47% | 0.3% | 0.4% | 0.47% | 0.34% | 0.54% | 0.46% | 0.71% | 0.79% |
| UMIs artificial diversity eliminated: | 79.12% | 66.63% | 68.56% | 65.91% | 65.11% | 68.07% | 66.51% | 59.69% | 71.21% | 69.5% | 78.27% |
| Reads dropped in UMI error correction and whitelist: | 0.02% | 0.01% | 0.01% | 0.01% | 0.01% | 0.01% | 0.0% | 0.01% | 0.01% | 0.01% | 0.01% |
| Reads dropped in tags filtering: | 0.79% | 0.34% | 0.46% | 0.3% | 0.39% | 0.46% | 0.34% | 0.54% | 0.45% | 0.7% | 0.78% |
| fileName | totalReads | totalClonotypes | clonesWithChain.TRA | clonesWithChain.TRB | clonesWithChain.TRD | clonesWithChain.TRG |
|---|---|---|---|---|---|---|
| 10_113046/10_113046.clns | 13029992 | 13455 | 6800 | 5900 | 109 | 571 |
| 11_113047/11_113047.clns | 6216885 | 61 | 26 | 34 | NA | 1 |
| 1_113026/1_113026.clns | 9004834 | 415 | 212 | 181 | 1 | 16 |
| 2_113030/2_113030.clns | 1416088 | 4 | 3 | 1 | NA | NA |
| 3_113032/3_113032.clns | 5922502 | 62 | 31 | 26 | NA | 5 |
| 4_113033/4_113033.clns | 1757522 | 47 | 28 | 17 | NA | 2 |
| 5_113034/5_113034.clns | 2811555 | 19 | 14 | 5 | NA | NA |
| 6_113037/6_113037.clns | 9302919 | 113 | 50 | 58 | NA | 5 |
| 7_113038/7_113038.clns | 11911843 | 967 | 442 | 489 | 4 | 16 |
| 8_113042/8_113042.clns | 12495147 | 1130 | 531 | 543 | 7 | 46 |
| 9_113044/9_113044.clns | 13514357 | 15987 | 7438 | 7963 | 100 | 324 |
The following samples were included in this analysis.
| 10_113046 |
| 11_113047 |
| 1_113026 |
| 2_113030 |
| 3_113032 |
| 4_113033 |
| 5_113034 |
| 6_113037 |
| 7_113038 |
| 8_113042 |
| 9_113044 |
This section shows repertoire statistical measures in each sample.
Table: The Volume column corresponds to the number of unique clonotypes in each sample.
Table: The Clones column corresponds to the total clonotype counts in each sample.
This section shows overlap of repertoires between samples. The two metrics used are the overlap between public or shared clonotypes and the Morisita overlap index.
Table: The values correspond to the number of shared clonotypes between two samples.
This section quantifies the usage of VDJ genes in the repertoire.
The top 10 used genes are selected by taking the mean frequency each gene is used across all datasets.
Table: The values correspond to the frequency of usage for each gene in each sample. The rows are ordered to show more frequently used genes first.
This section quantifies the similarity of gene usage across the samples. The metrics used are the Jensen-Shannon Divergence, which measures the dissimilarity between samples, and the gene usage correlation.
Table: The values correspond to the correlation metric of the gene usage between two samples.
This section quantifies commonly used metrics in species
(i.e.
The following samples were included in this analysis.
| 10_113046 |
| 11_113047 |
| 1_113026 |
| 2_113030 |
| 3_113032 |
| 4_113033 |
| 5_113034 |
| 6_113037 |
| 7_113038 |
| 8_113042 |
| 9_113044 |
This section shows repertoire statistical measures in each sample.
Table: The Volume column corresponds to the number of unique clonotypes in each sample.
Table: The Clones column corresponds to the total clonotype counts in each sample.
This section shows overlap of repertoires between samples. The two metrics used are the overlap between public or shared clonotypes and the Morisita overlap index.
Table: The values correspond to the number of shared clonotypes between two samples.
This section quantifies the usage of VDJ genes in the repertoire.
The top 10 used genes are selected by taking the mean frequency each gene is used across all datasets.
Table: The values correspond to the frequency of usage for each gene in each sample. The rows are ordered to show more frequently used genes first.
This section quantifies the similarity of gene usage across the samples. The metrics used are the Jensen-Shannon Divergence, which measures the dissimilarity between samples, and the gene usage correlation.
Table: The values correspond to the correlation metric of the gene usage between two samples.
This section quantifies commonly used metrics in species
(i.e.
The following samples were included in this analysis.
| 10_113046 |
| 11_113047 |
| 1_113026 |
| 2_113030 |
| 3_113032 |
| 4_113033 |
| 5_113034 |
| 6_113037 |
| 7_113038 |
| 8_113042 |
| 9_113044 |
This section shows repertoire statistical measures in each sample.
Table: The Volume column corresponds to the number of unique clonotypes in each sample.
Table: The Clones column corresponds to the total clonotype counts in each sample.
This section shows overlap of repertoires between samples. The two metrics used are the overlap between public or shared clonotypes and the Morisita overlap index.
Table: The values correspond to the number of shared clonotypes between two samples.
This section quantifies the usage of VDJ genes in the repertoire.
The top 10 used genes are selected by taking the mean frequency each gene is used across all datasets.
Table: The values correspond to the frequency of usage for each gene in each sample. The rows are ordered to show more frequently used genes first.
This section quantifies the similarity of gene usage across the samples. The metrics used are the Jensen-Shannon Divergence, which measures the dissimilarity between samples, and the gene usage correlation.
Table: The values correspond to the correlation metric of the gene usage between two samples.
This section quantifies commonly used metrics in species
(i.e.
Methods of unzip compressed files
Compressed files in the format of *.gz:
Unix/Linux/Mac user use “gzip *.gz” command
Windows user use uncompressed software such as WinRAR, 7-Zip et al
Compressed files in the format of *.zip:
Unix/Linux/Mac user use “unzip *.zip” command
Windows user use uncompressed software such as WinRAR, 7-Zip et al
How to operate different format data files
*.fastq reads sequence file, in the format of fasta. it is not easy to open since it is a large big file.
Unix/Linux/Mac users use less or more commands;
Windows users use editor Editplus/Notepad++ et al
.xls,.txt, *.tsv table result file; files are separated by(Tab)
Unix/Linux/Mac users use “less” or “more” commands
Windows users use editor Editplus/Notepad++ et al, also can use Microsoft Excel to open.
Software catalog:
FastQC v0.11.9
MiXCR v4.5.0
R V4.3.1
Reference
Cock P J A, Fields C J, Goto N, et al. (2010). The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic acids research 38, 1767-1771. (FASTQ)
Bolotin DA, Poslavsky S, Mitrophanov I, Shugay M, Mamedov IZ, et al. (2015) MiXCR: software for comprehensive adaptive immunity profiling. Nat Methods 12: 380.381. 10.1038/nmeth.3364
Shugay M, Bagaev D V., Turchaninova M a., Bolotin D a., Britanova O V., Putintseva E V., et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput Biol 2015;11:e1004503
Erlich Y, Mitra PP, delaBastide M, et al. (2008). Alta-Cyclic: a self-optimizing base caller for next-generation sequencing.Nat Methods. 2008 Aug;5(8):679-82.(sequencing error rate distribution)
Jiang L, Schlesinger F, Davis CA, et al. (2011). Synthetic spike-in standards for RNA-seq experiments.Genome Res. 2011 Sep;21(9):1543-51. (sequencing error rate distribution)
K
Parekh, S., Ziegenhain, C., Vieth, B., et al. (2016). The impact of amplification on differential expression analyses by RNA-seq. Scientific reports, 6(1), 1-11.
Fu, Y., Wu, P. H., Beane, T., et al. (2018). Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers. Bmc Genomics, 19(1), 1-14.
Kennedy, S. R., Schmitt, M. W., Fox, E. J., et al. (2014). Detecting ultralow-frequency mutations by Duplex Sequencing. Nature protocols, 9(11), 2586-2606.
Smith, T., Heger, A., & Sudbery, I. (2017). UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome research, 27(3), 491-499.
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